





"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


A			      =	{
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

A			      =	AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')

def      __A   (      a_    :Any ,							a_    :Union[str, Any]=False)		->		Any:
				__a							,    __a			:    str      							=	create_model(
				    '''HTSAT-tiny''' ,							'''roberta''' ,							a_ ,							precision='''fp32''' ,							device='''cuda:0''' if torch.cuda.is_available() else '''cpu''' ,							enable_fusion=a_ ,							fusion_type='''aff_2d''' if enable_fusion else None ,							)
				return model, model_cfg

def      __A   (      a_    :Union[str, Any])		->		List[str]:
				__a			:    str      							=	{}

				__a			:    str      							=	R'''.*sequential.(\d+).*'''
				__a			:    Optional[int]      							=	R'''.*_projection.(\d+).*'''

				for key, value in state_dict.items():
								# check if any key needs to be modified
								for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
												if key_to_modify in key:
																__a			:    List[Any]      							=	key.replace(a_ ,							a_)

								if re.match(a_ ,							a_):
												# replace sequential layers with list
												__a			:    str      							=	re.match(a_ ,							a_).group(1)

												__a			:    Union[str, Any]      							=	key.replace(F"""sequential.{sequential_layer}.""" ,							F"""layers.{int(a_)//3}.linear.""")
								elif re.match(a_ ,							a_):
												__a			:    Tuple      							=	int(re.match(a_ ,							a_).group(1))

												# Because in CLAP they use `nn.Sequential`...
												__a			:    Tuple      							=	1 if projecton_layer == 0 else 2

												__a			:    Optional[Any]      							=	key.replace(F"""_projection.{projecton_layer}.""" ,							F"""_projection.linear{transformers_projection_layer}.""")

								if "audio" and "qkv" in key:
												# split qkv into query key and value
												__a			:    Optional[Any]      							=	value
												__a			:    Tuple      							=	mixed_qkv.size(0) // 3

												__a			:    List[str]      							=	mixed_qkv[:qkv_dim]
												__a			:    Optional[int]      							=	mixed_qkv[qkv_dim : qkv_dim * 2]
												__a			:    Tuple      							=	mixed_qkv[qkv_dim * 2 :]

												__a			:    Union[str, Any]      							=	query_layer
												__a			:    int      							=	key_layer
												__a			:    Tuple      							=	value_layer
								else:
												__a			:    str      							=	value

				return model_state_dict

def      __A   (      a_    :str ,							a_    :Optional[int] ,							a_    :Optional[int] ,							a_    :Optional[Any]=False)		->		List[Any]:
				__a							,    __a			:    List[str]      							=	init_clap(a_ ,							enable_fusion=a_)

				clap_model.eval()
				__a			:    Optional[Any]      							=	clap_model.state_dict()
				__a			:    List[str]      							=	rename_state_dict(a_)

				__a			:    Optional[int]      							=	ClapConfig()
				__a			:    int      							=	enable_fusion
				__a			:    Optional[int]      							=	ClapModel(a_)

				# ignore the spectrogram embedding layer
				model.load_state_dict(a_ ,							strict=a_)

				model.save_pretrained(a_)
				transformers_config.save_pretrained(a_)


if __name__ == "__main__":
		A			      =	argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
		A			      =	parser.parse_args()

		convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
